Merge branch 'dev' of http://gitlab.pmf.kg.ac.rs/igrannonica/neuronstellar into dev

# Conflicts:
#	frontend/src/app/app.module.ts

10 files changed, 397 insertions, 103 deletions

diff --git a/backend/api/api/Controllers/ModelController.cs b/backend/api/api/Controllers/ModelController.cs
index ce1759ca..fb30a7a2 100644
--- a/backend/api/api/Controllers/ModelController.cs
+++ b/backend/api/api/Controllers/ModelController.cs
@@ -109,6 +109,24 @@ namespace api.Controllers
             return _modelService.GetMyModels(uploaderId);
         }
 
+        // GET: api/<ModelController>/mymodels
+        [HttpGet("mymodelsbytype/{problemtype}")]
+        [Authorize(Roles = "User")]
+        public ActionResult<List<Model>> GetMyModelsByType(string problemType)
+        {
+            string uploaderId = getUserId();
+
+            if (uploaderId == null)
+                return BadRequest();
+
+            List<Model> modeli = _modelService.GetMyModelsByType(uploaderId, problemType);
+            
+            if (modeli == null)
+                return NoContent();
+            else
+                return modeli;
+        }
+
         // vraca svoj model prema nekom imenu
         // GET api/<ModelController>/{name}
         [HttpGet("{name}")]
diff --git a/backend/api/api/Models/ColumnEncoding.cs b/backend/api/api/Models/ColumnEncoding.cs
index b5f61070..2a2fce8b 100644
--- a/backend/api/api/Models/ColumnEncoding.cs
+++ b/backend/api/api/Models/ColumnEncoding.cs
@@ -2,6 +2,12 @@
 {
     public class ColumnEncoding
     {
+        public ColumnEncoding(string columnName, string encoding)
+        {
+            this.columnName = columnName;
+            this.encoding = encoding;
+        }
+
         public string columnName { get; set; }
         public string encoding { get; set; }
     }
diff --git a/backend/api/api/Models/ColumnInfo.cs b/backend/api/api/Models/ColumnInfo.cs
index 99418732..04450fef 100644
--- a/backend/api/api/Models/ColumnInfo.cs
+++ b/backend/api/api/Models/ColumnInfo.cs
@@ -2,6 +2,20 @@
 {
     public class ColumnInfo
     {
+        public ColumnInfo() { }
+
+        public ColumnInfo(string columnName, bool isNumber, int numNulls, float mean, float min, float max, float median, string[] uniqueValues)
+        {
+            this.columnName = columnName;
+            this.isNumber = isNumber;
+            this.numNulls = numNulls;
+            this.mean = mean;
+            this.min = min;
+            this.max = max;
+            this.median = median;
+            this.uniqueValues = uniqueValues;
+        }
+
         public string columnName { get; set; }
         public bool isNumber { get; set; }
         public int numNulls { get; set; }
diff --git a/backend/api/api/Models/Experiment.cs b/backend/api/api/Models/Experiment.cs
index 6f665c52..f7bec083 100644
--- a/backend/api/api/Models/Experiment.cs
+++ b/backend/api/api/Models/Experiment.cs
@@ -10,6 +10,7 @@ namespace api.Models
         public string _id { get; set; }
         public string name { get; set; }
         public string description { get; set; }
+        public string type { get; set; }
         public List<string> ModelIds { get; set; }
         public string datasetId { get; set; }
         public string uploaderId { get; set; }
diff --git a/backend/api/api/Models/Predictor.cs b/backend/api/api/Models/Predictor.cs
index 8608d766..342c5b5d 100644
--- a/backend/api/api/Models/Predictor.cs
+++ b/backend/api/api/Models/Predictor.cs
@@ -21,9 +21,8 @@ namespace api.Models
 		public string modelId { get; set; }
 		public string h5FileId { get; set; }
 		public Metric[] metrics { get; set; }
-
-
 	}
+
 	public class Metric
     {
 		string Name { get; set; }
@@ -32,12 +31,15 @@ namespace api.Models
     }
 }
 
-/*
+/** 
+* Paste one or more documents here
+
 {
-	"_id" : "",
-		"username" : "ivan123",
-	  "name" : "Neki prediktor",
-		"description" : "Neki opis prediktora koji je unet tamo",
+	"_id": {
+		"$oid": "625dc348b7856ace8a6f8702"
+
+	},
+		"uploaderId" : "6242ea59486c664208d4255c",
 		"inputs": ["proba",
 		  "proba2", 
 		  "proba3"
@@ -46,6 +48,8 @@ namespace api.Models
 		"isPublic" : true,
 		"accessibleByLink" : true,
 		"dateCreated" : "2022-04-11T20:33:26.937+00:00",
-    "experimentId" : "Neki id eksperimenta"
-}
-*/
\ No newline at end of file
+    "experimentId" : "Neki id eksperimenta",
+    "modelId" : "Neki id eksperimenta",
+    "h5FileId" : "Neki id eksperimenta",
+    "metrics" : [{ }]
+}*/
\ No newline at end of file
diff --git a/backend/api/api/Services/FillAnEmptyDb.cs b/backend/api/api/Services/FillAnEmptyDb.cs
index 33c1ada6..6d5683bd 100644
--- a/backend/api/api/Services/FillAnEmptyDb.cs
+++ b/backend/api/api/Services/FillAnEmptyDb.cs
@@ -1,5 +1,6 @@
 using api.Interfaces;
 using api.Models;
+using Microsoft.AspNetCore.SignalR;
 using MongoDB.Driver;
 
 namespace api.Services
@@ -32,10 +33,10 @@ namespace api.Services
 
             if (_fileService.CheckDb())
             {
-                /*
+
                 FileModel file = new FileModel();
 
-                string folderName = "UploadedFiles/Igrannonica";
+                string folderName = "UploadedFiles";
                 var folderPath = Path.Combine(Directory.GetCurrentDirectory(), folderName, "Igrannonica");
                 var fullPath = Path.Combine(folderPath, "titanic.csv");
 
@@ -51,9 +52,9 @@ namespace api.Services
                 Dataset dataset = new Dataset();
 
                 dataset._id = "";
-                dataset.username = "Igrannonica";
+                dataset.uploaderId = "Igrannonica";
                 dataset.name = "Titanik dataset";
-                dataset.description = "Opis dataseta 1";
+                dataset.description = "Titanik dataset";
                 dataset.header = new string[] { "PassengerId", "Survived", "Pclass", "Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked" };
                 dataset.fileId = _fileService.GetFileId(fullPath);
                 dataset.extension = ".csv";
@@ -64,9 +65,25 @@ namespace api.Services
                 dataset.delimiter = "";
                 dataset.hasHeader = true;
                 dataset.columnInfo = new ColumnInfo[] { };
+                dataset.columnInfo = new[]
+                {
+                    new ColumnInfo( "PassengerId", true, 0, 446, 1, 891, 446, new string[]{ }),
+                    new ColumnInfo( "Survived", true, 0, 0.38383838534355164f, 0, 1, 0, new string[]{ }),
+                    new ColumnInfo( "Pclass", true, 0, 2.3086419105529785f, 1, 3, 3, new string[]{ }),
+                    new ColumnInfo( "Name", false, 0, 0, 0, 0, 0, new string[]{"Braund, Mr. Owen Harris", "Boulos, Mr. Hanna", "Frolicher-Stehli, Mr. Maxmillian", "Gilinski, Mr. Eliezer", "Murdlin, Mr. Joseph", "Rintamaki, Mr. Matti", "Stephenson, Mrs. Walter Bertram (Martha Eustis)", "Elsbury, Mr. William James", "Bourke, Miss. Mary", "Chapman, Mr. John Henry"}),
+                    new ColumnInfo( "Sex", false, 0, 0, 0, 0, 0, new string[]{ "male", "female" }),
+                    new ColumnInfo( "Age", true, 177, 29.69911766052246f, 0.41999998688697815f, 80, 28, new string[]{ }),
+                    new ColumnInfo( "SibSp", true, 0, 0.523007869720459f, 0, 8, 0, new string[]{ }),
+                    new ColumnInfo( "Parch", true, 0, 0.3815937042236328f, 0, 6, 0, new string[]{ }),
+                    new ColumnInfo( "Ticket", false, 0, 0, 0, 0, 0, new string[]{ "347082", "CA. 2343", "1601", "3101295", "CA 2144", "347088", "S.O.C. 14879", "382652", "LINE", "PC 17757" }),
+                    new ColumnInfo( "Fare", true, 0, 32.20420837402344f, 0, 512.3292236328125f, 14.45419979095459f, new string[]{ }),
+                    new ColumnInfo( "Cabin", false, 687, 0, 0, 0, 0, new string[]{ "B96 B98", "G6", "C23 C25 C27", "C22 C26", "F33", "F2", "E101", "D", "C78", "C93" }),
+                    new ColumnInfo( "Embarked", false, 2, 0.3815937042236328f, 0, 6, 0, new string[]{ "S", "C", "Q" }),
+                };
+                dataset.rowCount = 891;
                 dataset.nullCols = 3;
                 dataset.nullRows = 708;
-                dataset.isPreProcess = false;
+                dataset.isPreProcess = true;
 
                 _datasetService.Create(dataset);
 
@@ -74,25 +91,22 @@ namespace api.Services
                 Model model = new Model();
 
                 model._id = "";
-                model.username = "Igrannonica";
-                model.name = "Titanik model";
-                model.description = "Opis modela 1";
+                model.uploaderId = "Igrannonica";
+                model.name = "Model Titanik";
+                model.description = "Model Titanik";
                 model.dateCreated = DateTime.Now;
                 model.lastUpdated = DateTime.Now;
-                model.experimentId = "";
-                model.type = "regresioni";
-                model.encoding = "label";
+                model.type = "binarni-klasifikacioni";
                 model.optimizer = "Adam";
                 model.lossFunction = "mean_squared_error";
-                model.hiddenLayerNeurons = 1;
-                model.hiddenLayers = 1;
-                model.batchSize = 5;
+                model.hiddenLayerNeurons = 3;
+                model.hiddenLayers = 5;
+                model.batchSize = 8;
                 model.outputNeurons = 0;
-                model.hiddenLayerActivationFunctions = new string[] { "sigmoid" };
+                model.hiddenLayerActivationFunctions = new string[] { "relu", "relu", "relu", "relu", "relu" };
                 model.outputLayerActivationFunction = "sigmoid";
                 model.metrics = new string[] { };
                 model.epochs = 5;
-                model.isTrained = false;
 
                 _modelService.Create(model);
 
@@ -100,18 +114,35 @@ namespace api.Services
                 Experiment experiment = new Experiment();
 
                 experiment._id = "";
+                experiment.name = "Eksperiment Titanik";
+                experiment.description = "Binarno klasifikacioni, label";
+                experiment.ModelIds = new string[] { }.ToList();
                 experiment.datasetId = _datasetService.GetDatasetId(dataset.fileId);
                 experiment.uploaderId = "Igrannonica";
-                experiment.inputColumns = new string[] { };
-                experiment.outputColumn = "";
-                experiment.randomOrder = false;
-                experiment.randomTestSet = false;
-                experiment.randomTestSetDistribution = 0;
-                experiment.nullValues = "";
+                experiment.inputColumns = new string[] { "Embarked" };
+                experiment.outputColumn = "Survived";
+                experiment.randomOrder = true;
+                experiment.randomTestSet = true;
+                experiment.randomTestSetDistribution = 0.30000001192092896f;
+                experiment.nullValues = "delete_rows";
                 experiment.nullValuesReplacers = new NullValues[] { };
+                experiment.encodings = new[]
+                {
+                    new ColumnEncoding( "Survived", "label" ),
+                    new ColumnEncoding("Embarked", "label" )
+                };
 
                 _experimentService.Create(experiment);
 
+                var experiment1 = _experimentService.Get(experiment._id);
+                var dataset1 = _datasetService.GetOneDataset(experiment.datasetId);
+                var filepath1 = _fileService.GetFilePath(dataset.fileId, "Igrannonica");
+                var model1 = _modelService.GetOneModel(model._id);
+
+                
+                //_mlService.TrainModel(model1, experiment1, filepath1, dataset1, "Igrannonica");
+
+                /*
 
                 Predictor predictor = new Predictor();
 
@@ -127,7 +158,7 @@ namespace api.Services
                 predictor.experimentId = "0";
                 //izmeni experiment id
 
-                _predictorService.Create(predictor);
+                _predictorService.Create(predictor);*/
 
                 //--------------------------------------------------------------------
 
@@ -142,14 +173,13 @@ namespace api.Services
 
                 _fileService.Create(file);
 
-
+                
                 dataset = new Dataset();
 
                 dataset._id = "";
-                dataset.username = "Igrannonica";
+                dataset.uploaderId = "Igrannonica";
                 dataset.name = "Diamonds dataset";
-                dataset.description = "Opis dataseta 2";
-                dataset.header = new string[] { "carat", "cut", "color", "clarity", "depth", "table", "price", "x", "y", "z" };
+                dataset.description = "Diamonds dataset";
                 dataset.fileId = _fileService.GetFileId(fullPath);
                 dataset.extension = ".csv";
                 dataset.isPublic = true;
@@ -157,57 +187,91 @@ namespace api.Services
                 dataset.dateCreated = DateTime.Now;
                 dataset.lastUpdated = DateTime.Now;
                 dataset.delimiter = "";
-                dataset.hasHeader = true;
-                dataset.columnInfo = new ColumnInfo[] { };
+                dataset.hasHeader = true; 
+                dataset.columnInfo = new[]
+                 {
+                    new ColumnInfo( "Unnamed: 0", true, 0, 26969.5f, 0, 53939, 26969.5f, new string[]{ }),
+                    new ColumnInfo( "carat", true, 0, 0.7979397773742676f, 0.20000000298023224f, 5.010000228881836f, 0.699999988079071f, new string[]{ }),
+                    new ColumnInfo( "cut", false, 0, 0, 0, 0, 0, new string[]{ "Ideal", "Premium", "Very Good", "Good", "Fair" }),
+                    new ColumnInfo( "color", false, 0, 0, 0, 0, 0, new string[]{"G", "E", "F", "H", "D", "I", "I", "J"}),
+                    new ColumnInfo( "clarity", false, 0, 0, 0, 0, 0, new string[]{ "SI1", "VS2","SI2", "VS1", "VVS2", "VVS1", "IF", "I1"  }),
+                    new ColumnInfo( "depth", true, 0, 61.74940490722656f, 43, 79, 61.79999923706055f, new string[]{ }),
+                    new ColumnInfo( "table", true, 0, 57.457183837890625f, 43, 95, 57, new string[]{ }),
+                    new ColumnInfo( "price", true, 0, 3932.7998046875f, 326, 18823, 2401, new string[]{ }),
+                    new ColumnInfo( "x", true, 0, 5.731157302856445f, 0, 10.739999771118164f, 5.699999809265137f, new string[]{  }),
+                    new ColumnInfo( "y", true, 0, 5.73452615737915f, 0, 58.900001525878906f, 5.710000038146973f, new string[]{ }),
+                    new ColumnInfo( "z", true, 0, 3.538733720779419f, 0, 31.799999237060547f, 3.5299999713897705f, new string[]{ })
+                    };
+                dataset.rowCount = 53940;
                 dataset.nullCols = 0;
                 dataset.nullRows = 0;
-                dataset.isPreProcess = false;
+                dataset.isPreProcess = true;
 
                 _datasetService.Create(dataset);
 
-                */
-                /*
+                
+                
                 model = new Model();
 
                 model._id = "";
-                model.username = "Igrannonica";
-                model.name = "Igrannonica model 2";
-                model.description = "Opis modela 2";
+                model.uploaderId = "Igrannonica";
+                model.name = "Diamonds model";
+                model.description = "Diamonds model";
                 model.dateCreated = DateTime.Now;
                 model.lastUpdated = DateTime.Now;
-                model.experimentId = "";
-                model.type = "";
-                model.encoding = "";
-                model.optimizer = "";
-                model.lossFunction = "";
-                model.hiddenLayerNeurons = 0;
-                model.hiddenLayers = 0;
-                model.batchSize = 0;
+                model.type = "regresioni";
+                model.optimizer = "Adam";
+                model.lossFunction = "mean_absolute_error";
+                model.hiddenLayerNeurons = 2;
+                model.hiddenLayers = 4;
+                model.batchSize = 5;
                 model.outputNeurons = 0;
-                model.hiddenLayerActivationFunctions = new string[] { "sigmoid" };
-                model.outputLayerActivationFunction = "";
+                model.hiddenLayerActivationFunctions = new string[] { "relu", "relu", "relu", "relu" };
+                model.outputLayerActivationFunction = "relu";
                 model.metrics = new string[] { };
-                model.epochs = 0;
-                model.isTrained = false;
+                model.epochs = 5;
 
                 _modelService.Create(model);
-
+                
 
                 experiment = new Experiment();
 
                 experiment._id = "";
+                experiment.name = "Diamonds eksperiment";
+                experiment.description = "Diamonds eksperiment";
+                experiment.ModelIds = new string[] { }.ToList();
                 experiment.datasetId = _datasetService.GetDatasetId(dataset.fileId);
                 experiment.uploaderId = "Igrannonica";
-                experiment.inputColumns = new string[] { };
-                experiment.outputColumn = "";
-                experiment.randomOrder = false;
-                experiment.randomTestSet = false;
-                experiment.randomTestSetDistribution = 0;
-                experiment.nullValues = "";
-                experiment.nullValuesReplacers = new NullValues[] { };
-                
+                experiment.inputColumns = new string[] { "Unnamed: 0", "carat", "cut", "color", "clarity", "depth", "table", "x", "y", "z" };
+                experiment.outputColumn = "price";
+                experiment.randomOrder = true;
+                experiment.randomTestSet = true;
+                experiment.randomTestSetDistribution = 0.30000001192092896f;
+                experiment.nullValues = "delete_rows";
+                experiment.nullValuesReplacers = new NullValues[] { }; 
+                experiment.encodings = new[]
+                 {
+                    new ColumnEncoding( "Unnamed: 0", "label" ),
+                    new ColumnEncoding( "carat", "label" ),
+                    new ColumnEncoding( "cut", "label" ),
+                    new ColumnEncoding( "color", "label" ),
+                    new ColumnEncoding( "clarity", "label" ),
+                    new ColumnEncoding( "depth", "label" ),
+                    new ColumnEncoding( "table", "label" ),
+                    new ColumnEncoding( "price", "label" ),
+                    new ColumnEncoding( "x", "label" ),
+                    new ColumnEncoding( "y", "label" ),
+                    new ColumnEncoding( "z", "label" )
+                };
+
                 _experimentService.Create(experiment);
 
+                experiment1 = _experimentService.Get(experiment._id);
+                dataset1 = _datasetService.GetOneDataset(experiment.datasetId);
+                filepath1 = _fileService.GetFilePath(dataset.fileId, "Igrannonica");
+                model1 = _modelService.GetOneModel(model._id);
+                //_mlService.TrainModel(model1, experiment1, filepath1, dataset1, "Igrannonica");
+                /*
 
                 predictor = new Predictor();
 
@@ -224,12 +288,12 @@ namespace api.Services
                 //izmeni experiment id
 
                 _predictorService.Create(predictor);
-
+                */
                 //--------------------------------------------------------------------
 
                 file = new FileModel();
 
-                fullPath = Path.Combine(folderPath, "IMDB-Movie-Data.csv");
+                fullPath = Path.Combine(folderPath, "iris.csv");
                 file._id = "";
                 file.type = ".csv";
                 file.uploaderId = "Igrannonica";
@@ -242,10 +306,9 @@ namespace api.Services
                 dataset = new Dataset();
 
                 dataset._id = "";
-                dataset.username = "Igrannonica";
-                dataset.name = "Igrannonica dataset 3";
-                dataset.description = "Opis dataseta 3";
-                dataset.header = new string[] { "PassengerId", "Survived", "Pclass", "Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked" };
+                dataset.uploaderId = "Igrannonica";
+                dataset.name = "Iris dataset";
+                dataset.description = "Iris dataset";
                 dataset.fileId = _fileService.GetFileId(fullPath);
                 dataset.extension = ".csv";
                 dataset.isPublic = true;
@@ -254,56 +317,77 @@ namespace api.Services
                 dataset.lastUpdated = DateTime.Now;
                 dataset.delimiter = "";
                 dataset.hasHeader = true;
-                dataset.columnInfo = new ColumnInfo[] { };
-                dataset.nullCols = 0;
+                dataset.columnInfo = new[]
+                  {
+                    new ColumnInfo( "sepal_length", true, 0, 5.8433332443237305f, 4.300000190734863f, 7.900000095367432f, 5.800000190734863f, new string[]{ }),
+                    new ColumnInfo( "sepal_width", true, 0, 3.053999900817871f, 2, 4.400000095367432f, 3, new string[]{ }),
+                    new ColumnInfo( "petal_length", true, 0, 3.758666753768921f, 1, 6.900000095367432f, 4.349999904632568f, new string[]{ }),
+                    new ColumnInfo( "petal_width", true, 0, 1.1986666917800903f, 0.10000000149011612f, 2.5f, 1.2999999523162842f, new string[]{}),
+                    new ColumnInfo( "class", false, 0, 0, 0, 0, 0, new string[]{ "Iris-setosa", "Iris-versicolor", "Iris-virginica" }),
+                };
+                dataset.nullCols = 150;
                 dataset.nullRows = 0;
-                dataset.isPreProcess = false;
+                dataset.isPreProcess = true;
 
                 _datasetService.Create(dataset);
 
-
+                
                 model = new Model();
 
                 model._id = "";
-                model.username = "Igrannonica";
-                model.name = "Igrannonica model 3";
-                model.description = "Opis modela 3";
+                model.uploaderId = "Igrannonica";
+                model.name = "Model Iris";
+                model.description = "Model Iris";
                 model.dateCreated = DateTime.Now;
                 model.lastUpdated = DateTime.Now;
-                model.experimentId = "";
-                model.type = "";
-                model.encoding = "";
-                model.optimizer = "";
-                model.lossFunction = "";
-                model.hiddenLayerNeurons = 0;
-                model.hiddenLayers = 0;
-                model.batchSize = 0;
+                model.type = "multi-klasifikacioni";
+                model.optimizer = "Adam";
+                model.lossFunction = "sparse_categorical_crossentropy";
+                model.hiddenLayerNeurons = 3;
+                model.hiddenLayers = 3;
+                model.batchSize = 4;
                 model.outputNeurons = 0;
-                model.hiddenLayerActivationFunctions = new string[] { "sigmoid" };
-                model.outputLayerActivationFunction = "";
+                model.hiddenLayerActivationFunctions = new string[] { "relu", "relu", "softmax" };
+                model.outputLayerActivationFunction = "softmax";
                 model.metrics = new string[] { };
-                model.epochs = 0;
-                model.isTrained = false;
+                model.epochs = 1;
 
                 _modelService.Create(model);
-
+                
 
                 experiment = new Experiment();
 
                 experiment._id = "";
+                experiment.name = "Iris eksperiment";
+                experiment.description = "Iris eksperiment";
+                experiment.ModelIds = new string[] { }.ToList();
                 experiment.datasetId = _datasetService.GetDatasetId(dataset.fileId);
                 experiment.uploaderId = "Igrannonica";
-                experiment.inputColumns = new string[] { };
-                experiment.outputColumn = "";
-                experiment.randomOrder = false;
-                experiment.randomTestSet = false;
-                experiment.randomTestSetDistribution = 0;
-                experiment.nullValues = "";
-                experiment.nullValuesReplacers = new NullValues[] { };
+                experiment.inputColumns = new string[] { "sepal_length", "sepal_width", "petal_length", "petal_width" };
+                experiment.outputColumn = "class";
+                experiment.randomOrder = true;
+                experiment.randomTestSet = true;
+                experiment.randomTestSetDistribution = 0.20000000298023224f;
+                experiment.nullValues = "delete_rows";
+                experiment.nullValuesReplacers = new NullValues[] { }; 
+                experiment.encodings = new[]
+                 {
+                    new ColumnEncoding( "sepal_length", "label" ),
+                    new ColumnEncoding("sepal_width", "label" ),
+                    new ColumnEncoding( "petal_length", "label" ),
+                    new ColumnEncoding( "petal_width", "label" ),
+                    new ColumnEncoding( "class", "label" )
+                };
                 
                 _experimentService.Create(experiment);
 
+                experiment1 = _experimentService.Get(experiment._id);
+                dataset1 = _datasetService.GetOneDataset(experiment.datasetId);
+                filepath1 = _fileService.GetFilePath(dataset.fileId, "Igrannonica");
+                model1 = _modelService.GetOneModel(model._id);
+                //_mlService.TrainModel(model1, experiment1, filepath1, dataset1, "Igrannonica");
 
+                /*
                 predictor = new Predictor();
 
                 predictor._id = "";
diff --git a/backend/api/api/Services/IModelService.cs b/backend/api/api/Services/IModelService.cs
index bcb82e2d..00299979 100644
--- a/backend/api/api/Services/IModelService.cs
+++ b/backend/api/api/Services/IModelService.cs
@@ -8,6 +8,7 @@ namespace api.Services
         Model GetOneModel(string userId, string name);
         Model GetOneModel(string id);
         List<Model> GetMyModels(string userId);
+        List<Model> GetMyModelsByType(string userId, string problemType);
         List<Model> GetLatestModels(string userId);
         //List<Model> GetPublicModels();
         Model Create(Model model);
diff --git a/backend/api/api/Services/ModelService.cs b/backend/api/api/Services/ModelService.cs
index d3ff9bf9..c35e5374 100644
--- a/backend/api/api/Services/ModelService.cs
+++ b/backend/api/api/Services/ModelService.cs
@@ -35,6 +35,10 @@ namespace api.Services
         {
             return _model.Find(model => model.uploaderId == userId).ToList();
         }
+        public List<Model> GetMyModelsByType(string userId, string problemType)
+        {
+            return _model.Find(model => (model.uploaderId == userId && model.type == problemType)).ToList();
+        }
         public List<Model> GetLatestModels(string userId)
         {
             List<Model> list = _model.Find(model => model.uploaderId == userId).ToList();
diff --git a/backend/api/api/UploadedFiles/Igrannonica/iris.csv b/backend/api/api/UploadedFiles/Igrannonica/iris.csv
new file mode 100644
index 00000000..0713e5cb
--- /dev/null
+++ b/backend/api/api/UploadedFiles/Igrannonica/iris.csv
@@ -0,0 +1,151 @@
+sepal_length,sepal_width,petal_length,petal_width,class
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index 604e4d3c..f5122a06 100644
--- a/backend/microservice/api/newmlservice.py
+++ b/backend/microservice/api/newmlservice.py
@@ -156,6 +156,15 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
     #
     ### Enkodiranje
     encodings=paramsExperiment["encodings"]
+
+    from sklearn.preprocessing import LabelEncoder
+    kategorijskekolone=data.select_dtypes(include=['object']).columns
+    encoder=LabelEncoder()
+    for kolona in data.columns:
+        if(kolona in kategorijskekolone):
+            data[kolona]=encoder.fit_transform(data[kolona])
+    '''
+    encoding=paramsExperiment["encoding"]
     datafront=dataset.copy()
     svekolone=datafront.columns
     kategorijskekolone=datafront.select_dtypes(include=['object']).columns
@@ -207,6 +216,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
                         category_columns.append(col)
                 encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5)
                 encoder.fit_transform(data)
+
+    '''
     #
     # Input - output
     #
@@ -301,7 +312,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
 
         
 
-        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics'])
+        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse'])
 
         history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id']))
      
@@ -333,7 +344,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
             classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj
         classifier.add(tf.keras.layers.Dense(units=1, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj
 
-        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics'])
+        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse'])
 
         history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id']))
         hist=history.history
@@ -359,7 +370,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
             classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj
         classifier.add(tf.keras.layers.Dense(units=1))
 
-        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics'])
+        classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse'])
 
         history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id']))
         hist=history.history
@@ -529,7 +540,7 @@ def manageH5(dataset,params,h5model):
     h5model.summary()
     #ann_viz(h5model, title="My neural network")
 
-    h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['metrics'])
+    h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['accuracy',''])
 
     history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=params['batchSize'])